模型介绍
Ling-3.0-tiny English | 📖 中文文档 Abliterated APEX GGUF quants of inclusionAI/Ling-3.0-tiny — 7.9B total / 1.3B active hybrid-reasoning MoE Refusal direction removed with abliterix using LoRA steering search on a bnb-4bit loaded model. Over 130 candidate steering configurations were explored; the Pareto-selected recipe was merged into the BF16 weights. 85% fewer refusals (15/100 vs 98/100 baseline) at 0.0677 KL divergence. Post-abliteration capability spot-checks (math, logic, coding, translation, knowledge) all pass with no degradation observed. These GGUF files are quantized using APEX , a novel MoE-aware mixed-precision quantization technique that outperforms standard quantization methods while being significantly smaller. APEX beats Q8_0 perplexity at half the size — and even beats F16. APEX classifies every tensor by its role — routed expert, shared expert, or attention — and applies a layer-wise precision gradient, giving the most sensitive edge layers higher precision and compressing the redundant middle layers more aggressively. Ling-3.0-tiny's 128 routed experts (only 8 active per token) make it an ideal candidate.
主要特点
特点摘要待依据来源补齐，不凭模型名称推断能力。
局限与注意事项
暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。
来源报价与费用分析
同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。
| 来源 / 渠道 | 输入 | 输出 | 计费单位 | 采集时间 |
|---|---|---|---|---|
| 暂未取得可核验报价，请查阅来源渠道。 | ||||
按月估算基础 Token 费用
预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。
公开评测与运行配置
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
查看完整已收录榜单 →常见问题
开放权重是否代表可以免费商用？
不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。
这些价格是实时结算价格吗？
不是实时结算价格，而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。
资料来源与可追溯记录
许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。